In addition to these distance-dependent features, we also include indicator features that fire on bigrams s a j−1 , s a j and their word classes.

this feature can capture our intuition that, e.g., adjectives are more likely to come before or after a noun in different languages.


The training data contains mostly nontransliteration pairs and a few transliteration pairs.

the training data is initially very noisy and the joint sequence model is not very accurate.


In a head-driven strategy, one always starts parsing a production p from a fixed nonterminal in its rhs, called the head of p, and merges the remaining nonterminals one at a time with the partial parse containing the head.

under these strategies, the construction of partial parses that do not include the head is forbidden, and each parsing step involves at most one partial parse.


Accordingly, we use variables x H,e i , for each e i ∈ E, to denote the string components in tuples generated by H. For We let A i generate a tuple with two string components for each e j ∈ E(v i ).


In this arrangement, the modified cutwidth is attested by the number of edges crossing over the vertices in the left and right columns of the H-shaped graph, which is equal to where γ denotes the number of edges connecting vertices to the left with vertices to the right of the H-shaped graph.

bw(G) ≤ k if and only if mcw(G ′ ) ≤ k ′ .


However, the results we have presented in this paper do not carry over to the above mentioned synchronous models, since the fan-out of these models is bounded by two, while in our reductions in Section 3 we freely use unbounded values for this parameter.

the computational complexity of optimizing the choice of the parsing strategy for SCFGs is still an open problem.


Two strings γ 1 , γ 2 ∈ V * I are synchronous if each index from N occurs at most once in γ 1 and at most once in γ 2 , and index(γ 1 ) = index(γ 2 ).

γ 1 , γ 2 have the general form: ∈ I(N ), t i = t j for i = j, and π is a permutation of the set {1, .


The only nullable pairs of nonterminals in G prefix will then be of the form .

because of reduction, properness and consistency of G we have: because of the structure of the grammar transformation by which G prefix was obtained from G, we also have: pairs of occurrences of A ε 1 and A ε 2 with the same index in synchronous rules of G prefix can be systematically removed without affecting the probability of the resulting rule, as outlined in section 3.


On each round of constraint induction the cutting-plane algorithm computes the arg max in Equation 6 for a training instance, which is referred to as loss-augmented prediction, and adds the corresponding constraint to the active set.

the constraints from Equation (5) are equivalent to: if loss-augmented prediction turns up no new constraints on a given iteration, the current solution to the reduced problem,ŵ andξ, is the solution to the full SVM training problem.


Luckily, our choice of loss function, bigram recall, factors over bigrams.

we can easily perform loss-augmented prediction using the same procedure we use to perform Viterbi prediction (described in Section 4).


We omit from B(y) bigrams that are the result of deleted intermediate words.

the required number of variables z b is linear in the length of a sentence.


Sentence containing words that are sampled from high-level topics would be a better candidate for summary text.

; we present enriched TTM (ETTM) generative process (Fig.3), which samples words not only from low-level topics but also from high-level topics as well.


4 out of 45 models ETTM has the best performance in ranking the human generated sentences at the top, better than the TTM model.

; ETTM is capable of capturing focused sentences with general words related to the main concepts of the documents and much less redundant sentences containing concepts specific to user query.


The solutions found in this framework, however, do not necessarily satisfy a summary's budget constraint.

a subset of the solution found by solving Problem 2 has to be constructed as the final summary, and the near-optimality is no longer guaranteed.


Since the reference summaries are unknown, it is of course impossible to optimize F ROUGE-N directly.

some approaches (Filatova and Hatzivassiloglou, 2004;Takamura and Okumura, 2009;Riedhammer et al., 2010) instead define "concepts".


For example, in "the Seattle zoo", the entity mention "Seattle" modifies the noun "zoo".

the two mentions "Seattle" and "the Seattle zoo", are involved in what we later call a premodifier relation, one of several syntactico-semantic structures we identify in Section 3.


The contributions of this paper are summarized below: • We highlight that all relation types are expressed as one of several syntactico-semantic structures and show that most of these are relatively constrained and not difficult to identify.

working first in this structural dimension can be leveraged in the RE process to improve performance.


A major limitation of many of these systems is that they fail to distinguish between noun phrases and the underlying concepts they refer to.

a polysemous phrase like "apple" will refer sometimes to the concept Apple Computer (the company), and other times to the concept apple (the fruit).


In other words, (1) would be true if nobody eats, cows don't eat or meat is not eaten.

all three statements (a-c) are inside the scope of (1).


Second, we believe detecting the focus of negation is useful.

even though it is open to discussion, the focus corresponds to INSTRUMeNT(with a fork, ate) the negated statement should be interpreted as the cow ate grass, but it did not do so using a fork.


In other words, the set of hidden states F was chosen to be the fine set of treebank tags.

the number of fine tags varied across languages for our experiments; however, one could as well have fixed the set of HMM states to be a constant across languages, and created one mapping to the universal POS tagset.


2) before extracting the constraint features.

we are able to extract the constraint feature for all foreign word types and furthermore expect the projected tag distributions to be smoother and more stable.


As Figure 1 shows, this word has no high-confidence alignment in the Italian-English bitext.

its POS tag needs to be induced in the "No LP" case, while the  : Size of the vocabularies for the "No LP" and "With LP" models for which we can impose constraints.


This problem is NP-hard by a reduction from the NP-hard Transitive Subgraph problem (Yannakakis, 1978).

employing ILP is an appealing approach for obtaining an optimal solution.


To overcome this, we take advantage of graph sparsity: most predicates in language do not entail one another.

it might be possible to decompose graphs into small components and solve each Algorithm 1 Decomposed-ILP Input: A set V and a function f component separately.


Assume it does, then the violation is caused by omitting the edges in E cross .

there must be a node u ∈ U and w ∈ W (w.l.o.g) such that for some node v, (u, v) and (v, w) are in E new , but (u, w) is not.


However, this means either (u, v) or (v, w) is a crossing edge, which is impossible since we omitted all crossing edges.

e new is a better solution than e opt , contradiction.


(2010) presented a global optimization procedure to learn entailment rules between predicates using transitivity, and applied this algorithm over small graphs where all predicates have one argument instantiated by a target concept.

the rules they learn are of limited applicability.


In traditional ITG models, the branches of a biparse tree are generated from a nonterminal distribution, and each leaf is generated by a word or phrase pair distribution.

only minimal phrases are directly included in the model, while larger phrases must be generated by heuristic extraction methods.


We combine this model with the Bayesian nonparametric Pitman-Yor process (Pitman and Yor, 1997;Teh, 2006), realizing ITG-based divide and conquer through a novel formulation where the Pitman-Yor process uses two copies of itself as a base measure.

of this modeling strategy, phrases of multiple granularities are generated, and thus memorized, by the Pitman-Yor process.


Once the word alignment is fixed, the final permutation is uniquely determined, so we can safely discard that factor.

equation (8) can be written as: For the sole remaining factor P (F |D, E), we make the assumption that a segmented queried question F = w 1 , .


These parameters correspond to local cases where no linguistic consensus exists as to the proper gold annotation.

the standard evaluation does not provide a true indication of algorithm quality.


We show that these parameter changes can be mapped to edge direction changes in local structures in the dependency graph, and that these correspond to problematic annotations.

the standard evaluation measures do not reflect the true quality of the evaluated algorithm.


This is considered an error, since w 1 is neither w 3 's gold parent (as it is w 2 ), nor its gold child 9 .

one of the two tokens involved in the edge-flip is penalized by the measure.


Suppose that we have deduced the items OEi; h 1 ; k and OEk; h 2 ; j , where The item OEi; h 1 ; k asserts that for every configuration c 10 Item form: OEi; h; j , 0 Ä i Ä h < j Ä jwj Goal: OE0; 0; jwj Axioms: OEi; i; i C 1 OEi; h 1 ; k OEk; h 2 ; j Here,ˇ.c/ Dˇj , and .c/ D .c 10 /jh 1 .


However, if we aggregate responses of a single speaker, we have a better chance of finding a larger number of syntactic complexity features in the aggregated file.

we joined files from the same speaker to one file for the training set and the five test sets, resulting in 52 aggregated files in each test set.


This means that if a syllable is split into two consecutive non-prominent syllables, the agreement score may be higher than a long prominent syllable.

we introduce a weighting factor based on syllable duration (dur(i)).


Our approach, the lexically-triggered HMM (LT-HMM), is based on the fact that a code assignment is often indicated by short lexical triggers in the text.


The feature vector ψ B (A i , A s ) consists of eleven semantic featuresthe eight knowledge-based features plus LSA, ESA and a vector consisting only of tf*idf weights -both with and without question demoting.

the entire feature vector ψ(A i , A s ) contains a total of 30 features.


We focus on this generation due to the rise of popular social media technologies such as messaging and online social networks sites that occurred during that time.

we experimented with binary classification into age groups using all birth dates from 1975 through 1988, thus including students from generation Y who were in college during the emergence of social media technologies.


Furthermore, a blogger's posts can fall into two categories depending on his age at the time.

our second experiment looks at year of birth instead of age, as that never changes.


Grouping features by length was a simple but arbitrary method for reducing dimensionality, yet sometimes produced small bins of otherwise good features.

as we explored the feature space, small bins of different n-gram lengths were merged.


For an unsupervised approach, which only needs unlabeled data, there is little cost to creating large training sets.

this comparison of ACE-2/Ontonotes results is evidence that in a realistic scenario using association information in an unsupervised self-trained system is almost as good as a system trained on manually labeled data.


In the NAIST Text Corpus mention boundaries are not annotated, only the heads.

we considered as pseudo-mentions all bunsetsu chunks (i.e.


In other words, doing so could substantially simplify the coreference task.

we follow the approach adopted by traditional learning-based resolvers and employ an NP chunker to extract NPs.


We generally use bold-face characters (like t) for sequences, and we refer to their elements using subscripts (like t i ).

a tree t consists of a labeled root node σ followed by a sequence t of its children.


This feature is illustrated in Figure 8 and such an LMBOT can compute the transformation {(t, a) | t ∈ T Σ }, which cannot be computed by an STSG (assuming that T Σ is suitably rich).

sTsG with input desynchronization are more expressive than sTsG, but they still compute a class of transformations that is not closed under composition.


In a correct English parse tree, however, the subject-verb boundary is between "There" and "is".

tree-to-string translation based on constituent phrases misses the good translation rule.


We carry on until no more children can be generated.

the probability of a derivation tree T is For the minimal rule derivation tree in Figure 2, the probability is: Training We run the algorithm of Galley et al.


The word n-gram model does not have enough information to make the correct choice, with.

good translations might be pruned because of the beam.


Care must be taken when comparing these systems, because the differences in tag set definition results in different baselines.

we compare the % error reduction over the trivial baseline achieved in each case.


Note that crc(P ) is not necessarily injective; that is, there can be two full paths P 1 = P 2 satisfying crc(P 1 ) = crc(P 2 ).

in effect, score D (r | s) is determined by the best evidence of r; that is, Note that our final scoring function essentially views P as a clique rather than a path.


Despite the growing interest, research has been hindered by the lack of a large annotated corpus of learner text that is available for research purposes.

the standard approach to GEC has been to train an off-the-shelf classifier to re-predict words in non-learner text.


Previous work has either evaluated on artificial test instances as a substitute for real learner errors or on proprietary data that is not available to other researchers.

existing methods have not been compared on the same test set, leaving it unclear where the current state of the art really is.


The observed article or preposition choice of the writer is the class we want to predict.

the article or preposition cannot be part of the input features.


We stress that GoogleWeb does not contain complete sentences, but only n-gram counts.

we cannot generate training data for AP for feature sets Win3 and Win4: Since the algorithm does not assume feature independence, we need to have 7 and 9-word sequences, respectively, with a preposition in the middle (as shown in Table 1) and their corpus frequencies.


From each of these nodes, n + 1 arcs extend to each of the n context states and the end state.

the number of states in the language model is n + 2 and the number of arcs is O(n 2 ).


For each word length we distributed the probability of each possible spelling error equally.

for word length n, we have n deletion errors, 25n substitution errors, n − 1 transposition errors, and 26(n + 1) insertion errors, and the probability for each possible error is λn n+25n+n−1+26(n+1) .


There is a parameter for each word, which is the probability of that word being inserted.

we have 15 parameters for this noise model.


In the case where artificial lesioning is introduced, the lesions may not always reflect the actual errors found in human data, and it is difficult to replicate the actual tendency of humans to make a variety of different mistakes in a single sentence.

this method of evaluation, which may be suitable for evaluating the correction performance of specific grammatical errors, would not be fit for evaluating our model's overall performance.


One difference between Aspell and our model is that Aspell only corrects words which do not appear in the dictionary, while our method looks at all words, even those found in the dictionary.

our model can correct words which look correct by themselves, but seem to be incorrect due to the bigram context.


Given a name mention m, to perform entity linking, we need to find the entity e which maximizes the probability P(e|m).

then we can resolve the entity linking task as follows: the main problem of entity linking is to estimate the three distributions P(e), P(s|e) and P(c|e), i.e., to extract the entity knowledge from data.


For example, in following hyperlink (in Wiki syntax), the NBA is the name and the National Basketball Association is the referent entity.

we can get the training data by collecting all annotated name mentions from the hyperlink data of Wikipedia.


We consider each learned semantic role as a potential slot, and we extract slot fillers using the syntactic functions that were previously learned.

the learned syntactic patterns (e.g., the subject of release) serve the dual purpose of both inducing the template slots, and extracting appropriate slot fillers from text.


Although Araucaria has several limitations, such as rather small size and low agreement among annotators 2 , it is nonetheless one of the best argumentative corpora available to date.

premise: In this particular case, the individual a has property F and also property G. Conclusion: generally, if x has property F, then it also has property G. Argument from cause to effect Major premise: Generally, if A occurs, then B will (might) occur.


Minor premise: In this case, A occurs (might occur).

conclusion: in this case, B will (might) occur.


Conclusion: Therefore, A should (should not) be brought about.

argument from verbal classification Individual premise: a has a particular property F. Classification premise: For all x, if x has property F, then x can be classified as having property G. Conclusion: a has property G. arguments in araucaria are annotated in a XMLbased format called "aML" (argument Markup Language).


Moreover, this framework allows for detailed experimental studies of the interaction of specific linguistic features.

it has been demonstrated that for free word order languages like German, word order prediction quality can be improved with carefully designed, linguistically informed models capturing information-structural strategies (Filippova and Strube, 2007;Cahill and Riester, 2009).


By means of the extended underspecification rules for voice, the sentences in (2) and (3) receive an identical meaning representation.

our surface realiser can produce an active alternation for (3) and a passive alternation for (2).


We built 3 datasets from our alternation data: FS -candidates generated from the f-structure; SEM n -realisations from the naive meaning representations; SEM h -candidates from the heuristically underspecified meaning representation.

we keep the set of original corpus sentences (=the target realisations) constant, but train and test the model on different candidate sets.


This is not a trivial observation since a) the surface-independent features do not discriminate between the word orders and b) the precedence features are built from the same properties (see Section 4).

the SVM learner discovers depen-dencies between relative precedence preferences and abstract properties of a verb argument which cannot be encoded in the precedence alone.


Many dialogue acts can be used in authoritative or nonauthoritative ways, based on context, and can position a speaker as either giver or receiver of information.

these more general tagsets are not specific enough to the role of authority in discourse.


However, none were designed to specifically describe how people present themselves as a source or recipient of knowledge in a discourse.

we have chosen to draw on a different field of sociolinguistics.


Thus, if we can automatically detect the collocations in the sentence to be translated and their orders in the target language, the reordering information of the collocations could be used to constrain the reordering of phrases during decoding.

in this paper, we propose to improve the reordering model for SMT by estimating the reordering score based on the translation orders of the source collocations.


The collocation (基本, 立场) is included in the same phrase and translated together as a whole.

its translation is correct in both translations.


The open gap represented by serves a similar purpose as the non-terminal categories in a hierarchical phrase-based system such as Hiero.

it generalizes to translate "eine zeitung" in exactly the same way as "ein buch".


A Jump Back (W) operation is only allowed at position Z.

if j = Z, a Jump Forward operation has to be performed prior to a Jump Back operation.


However, given only the source sentence (and little world knowledge), it is not realistic to try to model the reasons for all of this reordering.

we can use a more robust model that reorders less than humans.


One possible reason for this lies in the design decision of Negra to use relatively flat tree structures.

many structures that in other treebanks would be prepositional phrases with embedded noun phrases -and thus non-local constituents -are flat prepositional phrases here.


(2004) used definition sentences in two manually compiled dictionaries, which are considerably fewer in the number of definition sentences than those on the Web.

the coverage of their method should be quite limited.


In preparing the training data, we faced the problem that the completely random sampling of candidate paraphrase pairs provided us with only a small number of positive examples.

we automatically collected candidate paraphrase pairs that were expected to have a high likelihood of being positive as examples to be labeled.


Though our method can extract a variety of paraphrase instances on a large scale, their coverage might be insufficient for real NLP applications since some paraphrase phenomena are highly productive.

we need paraphrase rules in addition to paraphrase instances.


Unfortunately, there is little consensus in the literature on such definitions.

we follow (van Halteren and Teufel, 2003) and make the following distinction.


Intuitively, words that appear in similar contexts are more similar to each other and will have a stronger edge between them in the network.

similar words, or words that appear in similar contexts, will form communities in this graph.


The similarity on such vectors x and y is then quantified as: Clause-Level Constraints Arguments occurring in the same clause cannot bear the same role.

clusters should not merge if the resulting cluster contains (many) arguments of the same clause.


Performance also increases if gold standard arguments are used instead of automatically identified arguments.

each model attains its best scores on the gold/gold dataset.


It is noteworthy that global information from the Web is only used to measure the entity-background consistency and not directly in the inference process.

our event extraction system actually still performs a sentence-level inference based on local information.


Besides, as discussed in section 4, entities of the same type normally take on the same role in similar events, especially when they co-occur with similar arguments in the events (see Table 2).

all instances of co-occurrence model {entity subtype, event type, arguments} in training corpus could provide effective evidences for predicting the role of argument in the candidate event mention.


The coefficient calculate the frequency of a trigger (or a role) appearing in specific domain of event mentions and that in whole training corpus, then combines them to represent its confidence degree, just like TFIDF algorithm.

the more typical triggers (or roles) will be given high confidence.


Actually, we select the sentence which at least includes one entity mention as candidate event mention, but lots of event mentions in ACE never include any entity mention.

we have missed some mentions at the starting of inference process.


For example, the subtype 1 of "Air" in Table 5 lost the entities of "MiGs" and "enemy planes", but involved "terrorist", "Saddam", etc (See Table 9).

we manually clustered the subtypes and retry the step-by-step cross-entity inference.


The event extraction task is to find any description of a relevant event, even if the event is not the topic of the article.

1 all documents are given to the event sentence recognizers and their mission is to identify any sentence that mentions a relevant event.


First, we noticed that event narratives tend to mention relevant information within the first several sentences, whereas fleeting reference texts usually mention relevant information only in the middle or end of the document.

our first heuristic requires that an event narrative mention a role filler within the first 7 sentences.


For example, while Freebase contains 116 million instances of 7,300 relations for 9 million entities, it only covers 48% of the slot types and 5% of the slot answers in KBP2010 evaluation data.

both CUNY and LCC observed limited gains from the answer validation approach from Freebase.


For example, Mount Vesuvius and Kilimanjaro are volcanoes and Lhotse is similar to them, but Lhotse is not a volcano.

we should be very conservative and careful in hypernym propagation.


It is easy to prove (by following a similar procedure) that the above Formula holds for the case of m>2, as long as the pieces of evidence are mutually independent.

for a term-label pair with m mutually independent supporting sentences, if we set every gain G(A|E i ) to be a constant value g, the posterior gain score of the pair will be ∑ .


Once we have observed S 1 , however, the chance of (T, L) having a hyponymy relation in-creases.

the chance of observing another supporting sentence becomes larger than before.


The basic idea is that the hidden states of FHMMs are an explicit short-term memory with an antecedent buffer containing recently described referents.

an observed pronoun can find its antecedent from the hidden buffer, or in terms of a generative model, the entries in the hidden buffer generate the corresponding pronouns.


According to Clark and Sengul (1979), anaphoric definite NPs are much faster retrieved if the antecedent of a pronoun is in immediately previous sentence.

a limited short term memory should be good enough for resolving the majority of pronouns.


The main point of comparison is Denis and Baldridge (2007), which was similar in that it described a new type of coreference resolver using simple features.

similar to their practice, we use all forms of personal and possessive pronouns that were annotated as ACE "markables".


But the gap between its results and results of our system is large.

we may still say that our system probably can do a better job even if we train new models files for emPronouns with ACE corpus.


The FHMM system always maintain a set of mentions as well as a first-order dependencies between part of speech and operator.

context can be more fully taken into consideration.


Referring to models in terms of their parametrizations is potentially confusing.

we will make use of the fact that models with α C = 1 yielded roughly 40 label clusters on average, and models with α C = 0.1 yielded roughly 200 label clusters, naming model variants simply by the number of query and label clusters: (1) CLC-BASE, (2) CLC-DPMM 1C-40L, (3) CLC-HDP-LG 100C-40L, (4) CLC-HDP-LG 1000C-40L, and (5) CLC-HDP-LG 1000C-200L.


This in itself can affect learner corpus creation much more than one would expect especially when creating a longitudinal learner corpus.

it is crucial to keep learners motivated and focused on the writing assignments.


However, this is not the case when the texts are first collected.

it is better to have an efficient method for managing such information as well as the texts themselves.


The instance, however, appears in a position where a verb appears.

there are at least two possibilities for tagging: success/NN and success/VB.


In the pilot study, we used the same learner corpus as in the pilot study for the error annotation scheme.

of the pilot study, we found that the Penn Treebank tag set sufficed in most cases except for errors which learners made.


Through out the exercises, they did not have access to the others' essays and their own previous essays.

233 essays were collected; Table 3 shows the statistics on the collected essays.


This discrepancy sometimes occurred because the tokenization of the system sometimes differed from that of the human annotators.

19 and 126 sentences (215 and 1,352 tokens) were excluded from the evaluation in the HMM-based and CRF-based POS taggers, respectively.


This makes it difficult to determine if a Turker is qualified to complete a translation task.

soliciting translations from anonymous non-professionals carries a significant risk of poor translation quality.


Most of the Turkers performing our task were native Urdu speakers whose second language was English, and they do not always produce natural-sounding English sentences.

the first set of features attempt to discriminate good English sentences from bad ones.


As an example, Figure 1 shows a syntactic parse tree with the prefix order (i.e., the number at the up-right corner of each non-terminal node), where child nodes are visited recursively from left to right before the parent node is visited.

the NP-SBJ node is visited first, followed by the NP spanning duo action, followed by the PP-CLR node etc.


Although standard implementations of phrase-based decoders fall out of this scope, they can be still re-written to work in the CKY-style bottom-up manner at the cost of 1) only BTG-style reordering allowed, and 2) higher time complexity.

any phrase-based SMT system can be used as a component in our HM decoding method.


In this task, the output of the component systems are single hypotheses or unweighted lists thereof.

we lack of the statistics of the components' posteriors which is one of the main advantages of MBR system combination over system combination techniques.


Note that his MI trigger model is distance-dependent since trigger pairs (w k , w i ) are sensitive to their distance i − k − 1 (zero distance for adjacent words).

the distance between word x and word y should be taken into account when calculating their PMI.


while the PMI value of the trigger pair (is, is) is as high as 0.32.

our MI trigger model selects "is" rather than "was".


Note, however, that it is not used when a quantity or a number word is present, e.g., dua pelajar/'two students' and banyak pelajar/'many students'.

if we do not know how to translate pelajar-pelajar, it would be reasonable to consider the translation options for pelajar since it could potentially contain among its translation options the plural 'students'.


More precisely, we use pivoting to estimate the probability Pr(w ′ |w) as follows: Then, following (Callison-Burch et al., 2006;Wu and Wang, 2007), we make the simplifying assumption that w ′ is conditionally independent of w given e i , thus obtaining the following expression: We estimate the probability Pr(e i |w) directly from the word-aligned training bi-text as follows: where #(x, e) is the number of times the Malay word x is aligned to the English word e. Estimating Pr(w ′ |e i ) cannot be done directly since w ′ might not be present on the Malay side of the training bi-text, e.g., because it is a multi-token sequence generated by clitic segmentation.

we think of w ′ as a pseudoword that stands for the union of all Malay words in the training bi-text that are reducible to w ′ by our morphological analysis procedure.


For some of the words, e.g., the lemmata, there could already be such phrases, but for other transformations, e.g., clitic segmentation, this is unlikely.

we need to augment the phrase table with additional translation options.


(Dyer, 2007) 23   We created our Malay-English training and development datasets from data that we downloaded from the Web and then sentence-aligned using various heuristics.

we ended up with 350,003 training sentence pairs, including 10.4M English and 9.7M Malay word tokens.


Unfortunately, none of these general lines of research suits Malay well, whose compounds are rarely concatenated, clitics are not so frequent, and morphology is mostly derivational, and thus likely to generate words whose semantics substantially differs from the semantics of the original word.

we cannot expect the existence of equivalence classes: it is only occasionally that two derivationally related wordforms would share the same target language translation.


Finally, when we introduce gappy state phrases of the type s i s j , the number of such phrases is O(m 2 ), since we may choose a start and end point independently.

the total complexity rises to O((Km + m 2 ) 2 Kn) = O(Km 4 n).


This clearly indicates that interference effects from one source language might be misleading when used to identify translations from a different language.

for example, in the Finnish corpus, the word me is a strong indicator of original English (constituting 0.0003 of tokens in texts translated from Finnish as opposed to 0.0015 of tokens in original English texts), but in the German corpus, me is an indicator of translated text (constituting 0.0020 of tokens in text translated from German).


The most interesting result that can be seen in this table is that the accuracy obtained when training using language x and testing using language y depends precisely on the degree of similarity between x and y.

for training and testing within the three cognate languages, results are fairly strong, ranging between 84.5% and 91.5%.


This shows that there are features of translationese that cross genres and widely disparate languages.

for one prominent example, we find that, as in Europarl, the word the is overrepresented in translationese in IHT (15.36% in T vs. 13.31% in O; significant at p<0.01).


In our case, the number of co-occurrences will be limited by the number of occurrences of the words, from 1 to 5.

the Jaccard similarity efficiently reflects what we want to observe.


Decoupling machine translation (MT) and TE, in fact, ties CLTE performance to the availability of MT components, and to the quality of the translations.

on one side translation errors propagate to the TE engine hampering the entailment decision process.


We assume here that no information about the authors of the document is available and that in particular we are not supplied with any identified samples of any author's writing.

our methods must be entirely unsupervised.


However, synonyms are relatively sparse and hence, though reliable, they are not comprehensive; that is, they are useful for separating out some units but not all.

we use a twostage process: first find a reliable partial clustering based on synonym usage and then use these as the basis for supervised learning using a different feature set, such as bag-of-words.


and we can't expect an unsupervised method to read our minds.

to guide the method in the direction of stylistic elements that might distinguish between Jeremiah and Ezekiel, we define a class of generic biblical words consisting of all 223 words that appear at least five times in each of ten different books of the Bible.


Repeating our experiment of above, though limiting our feature set to generic biblical words, we obtain the following matrix: Book Cluster I Cluster II Jer Eze 32 28 20 20 As can be seen, using generic words yields NMD of 51.3%, which does not improve matters at all.


Conveniently, and unlike most modern translations, KJV almost invariably translates synonyms identically.

we can generally identify synonyms by considering the translated version of the text.


Conveniently, Strong's (1890Strong's ( [2010) Concordance lists every occurrence of each sense of each root that appears in the Bible separately (where senses are distinguished in accordance with the KJV translation).

we can exploit KJV and the concordance to automatically identify synsets as well as occurrences of the respective synonyms in a synset.


If j different synonyms in a synset appear in the unit, then each corresponding entry is 1/j and the rest are 0.

in the typical case where exactly one of the synonyms in a synset appears, its corresponding entry in the vector is 1 and the rest are 0.


The point to recall is that while this feature set proved inadequate in an unsupervised setting, this does not mean that it is inadequate for separating Jeremiah and Ezekiel, given a few good training examples.

we use a bag-of-words representation restricted to generic Bible words for the 74 units in our cluster cores and label them according to the cluster to which they were assigned.


We find that the multi-output lasso and the full vocabulary regression are not significantly different on any of the attributes.

the multioutput lasso achieves a 93% compression of the feature set without a significant decrease in predictive performance.


The fraction of solvable mentions increases until about five candidates per mention are generated, after which the increase is rather slow.

we believe choosing a limit of 20 candidates per mention offers an attractive trade-off of accuracy and efficiency.


The large combined search space of joint word segmentation and Part-of-Speech (POS) tagging makes efficient decoding very hard.

effective high order features representing rich contexts are inconvenient to use.


(2007) showed the effectiveness of utilizing syntactic information to rerank POS tagging results.

the capability to represent rich contextual features is crucial to a POS tagger.


Our experiments on the Penn Chinese Treebank will show that the word-based and character-based segmenters and the local tagger on their own produce high quality word boundaries.

the oracle performance to recover words from a subword sequence is very high.


The statistics will also empirically show that subwords are significantly larger than characters and only slightly smaller than words.

the search space of the sub-word tagging is significantly shrunken, and exact Viterbi decoding without approximately pruning can be efficiently processed.


Idiom In linguistics, idioms are usually presumed to be figures of speech contradicting the principle of compositionality.

it is very hard to recognize out-of-vocabulary idioms for word segmentation.


determining the optimal segmentation is sufficient for finding the constituents.


While this may work for some languages, the subwords are not valid words in general as discussed in Section 1.1.3.

we allow the lexemes to be the result of a transformation process, where the transformed lexemes are denoted by g K 1 .


The explicit dependence on position n k−1 in Equation 6 allows us to determine if we are at the beginning, in the middle, or at the end of a token.

we can distinguish between start, middle, or end morphemes and hypothesize them during search.


In fact, all these words are composed of the prefix 䉂 'vice' and a root word.

the structure of 䉂擌奒 'vice president' can be represented with the tree in ment on the correctness of this structure among native Chinese speakers.


The same thing happens for natural language processing systems.

it is necessary to have a dynamic mechanism for parsing word structures.


Unfortunately, this treebank or any other treebanks for that matter, does not contain annotations of word structures.

we must annotate these structures by ourselves.


For example, during the process of introducing new features in a search engine it is important to preserve users' confidence in the system.

a system must decide whether it should give or not a result in the new fashion or keep on with the old kind of output.


This is represented in contingency Table 1, where: • n ac : number of questions for which the answer is correct • n aw : number of questions for which the answer is incorrect • n u : number of questions not answered • n: number of questions (n = n ac + n aw + n u ) Let's start studying a simple utility function able to establish the preference order we want: • -1 if question receives an incorrect response • 0 if question is left unanswered • 1 if question receives a correct response Let U(i) be the utility function that returns one of the above values for a given question i.

if we want to consider n questions in the evaluation, the measure would be: The rationale of this utility function is intuitive: not answering adds no value and wrong answers add negative values.


We can use this observation as our estimation for P (C/¬A) instead of the arbitrary value of 0.5.

the measure we propose is c@1 (correctness at one) and is formally represented as follows: The most important features of c@1 are: 1.


Let BIN(d) denote a mapping from a difference d to one of the 21 bins where it belongs.

algorithm in Figure 3 is applied for calculating the swap-rate of each bin.


First, we softly label those vertices of the constructed graph for which frame distributions are available from the FrameNet data (the same distributions that are used to compute sim F N ).

initially, a small fraction of the vertices in the graph have soft frame labels on them.


Note that in all our experiments, we assume that the targets are marked in a given sentence of which we want to extract a frame-semantic analysis.

unlike the systems presented in SemEval'07, we do not define a target identification module.


We extend this task and treat semantic parsing as recursive prediction of predicate-argument structure and clustering of argument fillers.

parsing a sentence into this representation involves (1) decomposing the sentence into lexical items (one or more words), (2) assigning a cluster label (a semantic frame or a cluster of argument fillers) to every lexical item, and (3) predicting argument-predicate relations between the lexical items.


A number of these instances will be members of both one gold standard class and one cluster.

the quality of the proposed clustering solution is evaluated by comparing the two groupings and measuring their similarity.


However, it is impractical for people to identify the important aspects from the numerous reviews manually.

it becomes a compelling need to automatically identify the important aspects from consumer reviews.


Classes are only needed in cases where an event was not seen or was not frequent enough in the training set.

we should form clusters not based on all events in the training corpus, but only on events that are rare -because this is the type of event that classes will then be applied to in prediction.


We use a Binomial variable π to control the proportion be-tween content and functional topics in each sentence.

there are k+1 topic transitions, one for T-START and others for k content topics; and k emission probabilities for the content topics, with an additional one for the functional topic z B (in total k+1 emission probability distributions).


Since we already observe topic z and sentence s cooccur with probability p(s, z|d, Θ), each word w in s should share the same probability of being observed with content topic z.

the expected count of c(z, w) in this sentence would be p(s, z|d, Θ)c(w, s).


Since both the topic transition and emission probabilities are Multinomial distributions in strTM, the conjugate Dirichlet distribution is the natural choice for imposing a prior on them (Diaconis and Ylvisaker, 1979).

we further assume: where we use exchangeable Dirichlet distributions to control the sparsity of α z and β z .


Since only a small portion of sentences are annotated in the Review data set, very few neighboring sentences are annotated at the same time, which introduces many noisy transitions.

we only performed the comparison on the Ads data set.


The results demonstrate that CoRank relies on both the information from the same language side and the information from the other language side for sentence ranking.

both the Chinese-side information and the English-side information can complement each other, and they are beneficial to the final summarization performance.


that they are represented by the respective corpora.

a corpus has been considered a homogeneous unit.


However, it is likely that from a machine-learning perspective, representing similar categories with the same tag may be useful for learning.

we next experimented with modifying inflectional features that proved most useful.


In case of ties, the scores of the class labels from L H1 (I) serve as a secondary ranking criterion.

every instance I from the IsA repository is associated with a ranked list of class labels computed according to this ranking formula.


Among the query-based runs R p , R s and R u , the highest scores in Table 4 are obtained mostly for run R s .

between the presence of a class label and an instance either in the same query, or as separate queries within the same query session, it is the latter that provides a more useful signal during the reranking of class labels of each instance.


For example, in the fragment "by enhancing its inactivation through binding to soluble TNF-alpha receptor type II," the protein TNF-alpha receptor type II is an argument in both a BINDING event (binding) and in a NEGATIVE REGULATION event (inactivation).

of this step, 4.7% of the events in training are removed.


On the other hand, investigating large amounts of data is a timeconsuming job.

a comparison mining system, which can automatically provide a summary of comparisons between two (or more) entities from a large quantity of web documents, would be very useful in many areas such as marketing.


We sum up our proposed method for Task 1 as two steps as follows; 1) The comparison lexicon detects CS-candidates in text documents, and then SVM eliminates the non-comparative sentences from the candidates.

all of the sentences are divided into two classes: a comparative class and a non-comparative class.


For example, consider the trigram model schematic shown in Figure 1, in which only history sequences of length 2 are kept in the model.

from history h i = w i−2 w i−1 , the word w i transitions to h i+1 = w i−1 w i , which is the longest suffix of h i w i in the model.


Whenever the seed does not contain the examples of a certain cluster that is representative of a group of examples in the data, the learner may become overconfident about the class membership of this cluster (particularly if it lies far from the decision boundary).

the learner spends a lot of time exploring one region of the instance space at the expense of missing another.


If 10 examples are chosen randomly for seeding AL, the probability that none of the rare class examples will make it to the seed is 60% 1 .

there is a high probability that AL would stall, selecting only the examples of the predominant class over the course of many iterations.


In supervised word sense disambiguation, a single model per word is typically trained and that is the approach we take.

we conduct our evaluation using 25 different data sets.


Observe that for LM-seeded AL, the recall peaks at first and begins to decline later.

the classifier makes progressively more errors on the rare class as more labeled examples are being received.


This approach selects more rare class examples than random sampling, resulting in more rapid learning and, more importantly, leading to a classifier that performs better on rare class examples.

of this, the overall classification accuracy is higher than that for classic AL.


Even if a chance of approximation error were introduced for each hash function, this may be justified in greater overall fidelity from the resultant increase in b.

we propose to approximate the online hash function, using a novel technique we call Reservoir Counting, in order to create a space trade-off between the number of projections and the amount of memory each projection requires.


We set out to develop a POS inventory for Twitter that would be intuitive and informative-while at the same time simple to learn and apply-so as to maximize tagging consistency within and across an-notators.

we sought to design a coarse tagset that would capture standard parts of speech 3 (noun, verb, etc.)


For example, apostrophes are often omitted, and there are frequently words like ima (short for I'm gonna) that cut across traditional POS categories.

we opted not to split contractions or possessives, as is common in English corpus preprocessing; rather, we introduced four new tags for combined forms: {nominal, proper noun} × {verb, possessive}.


classification time is proportional to the number of labeled data points.


However, a sample of labeled data may not include data points that are near the center of a cluster.

cNN sometimes needs several points to stabilize the representation of a cluster; e.g.


In fact for any factor f ∈ F k (⋊w⋉), it is the case that |w| f = |v| f .


For example if [i] is opaque, this can be described by forbidding factors {iu, ioöu,öo, uö, oö} on the vowel tier.

words like lulolilö are acceptable because oi is a permissible factor.


For example, a sentence of the form "[subject] stuck [object] in" or "[subject] could eat [object] all day" is more likely to be a TWSS than not.

we hypothesize that machine learning with euphemismand structure-based features is a promising approach to solving the TWSS problem.


As such, DEviaNT is at a disadvantage to many potential solutions because DE-viaNT does not aggressively explore features specific to TWSSs (e.g., DEviaNT does not use a lexical n-gram model of the TWSS training data).

the goal of our evaluation is not to outperform the baselines in all aspects, but rather to show that by using only euphemism-based and structure-based features, DEviaNT can compete with the baselines, particularly where it matters most, delivering high precision and few false positives.


In particular, its training data were noisy, partially untaggable, and multilingual.

we reimplemented this approach more rigorously as one of our baselines.


The hypothesis generation thus proceeds as follows: • For a given sentence, let the base sequence labeler generate up to k s sequences of unlabeled opinion expressions; • for every sequence, apply the base polarity classifier to generate up to k p polarity labelings.

the hypothesis set size is at most k s • k p .


Banea et al already noted: "We have seen a surge in interest towards the application of automatic tools and techniques for the extraction of opinions, emotions, and sentiments in text (subjectivity)" (p. 127) (Banea et al, 2008).

it is timely and useful to examine subjective natural language problems from different angles.


Evaluations of each system were carried out on different, although similar, corpora.


Since we are interested in general query formulation behavior, we do not want our analysis to be dominated by trends in popular intents.

we take macro-average of Q-level over different URLs in a given month, and our main aim is to explore long-term temporal changes in this value.


Since the early days of natural language understanding (NLU), quantifier scope disambiguation has been an extremely hard task.

early NLU systems either devised some mechanism for leaving the semantic representation underspecified (Woods 1978, Hobbs andShieber 1987), or tried to assign scoping to sentences based on heuristics (VanLehn 1978, Moran 1988, Alshawi 1992.


are soft constraints, that is they define a preference over the possible readings of a sentence.

statistical methods seem to be well suited for scope disambiguation.


Most work on quantifier scoping focuses on scoping phenomena, which may be interesting from theoretical perspective, but do not occur very often in practice.

many challenging practical phenomena remain unexplored.


For example, if the target language is first order logic with generalized quantifiers, the relative scope of the chunks labeled NI does not affect the interpretation.

2 we define a new version of observed agreement in which we consider a pair a match if it is labeled NI in one scoping or assigned the same label in both scopings.


However, in written Chinese text, there is no word delimiter.

in almost all tasks of Chinese natural language processing (NLP), the first step is to segment a Chinese sentence into a sequence of words.


Our experimental results have shown that character-level metrics correlate better with human assessment than word-level metrics.

cWS is not needed for automatic evaluation of chinese translation output.


Although parameter learning in our framework would be another interesting topic to study, we focus herein on the inference problem.


This variation in the parameter vector affects the quality of the model measured on both development data and held-out test data, independently of any experimental manipulation.

when trying to determine whether the difference between two measurements is significant, it is necessary to control for variance due to noisy parameter estimates.


Crucially, this assumes that the samples being analyzed are representative of all extraneous variables that could affect the outcome of the experiment.

we must include multiple optimizer replications.


In fact, some of the new feature templates in this paper, such as distance and valency, are among those which are in the graph-based submodel of Zhang and Clark (2008), but not the transition-based submodel.

our new features to some extent achieved the same effect as their model combination.


Conversely, in generation the model is conditioned on the input logical form l, p(d|l) (Velldal et al., 2004).

in such directional stochastic attribute-value grammars, the probability of a derivation d given an input x (a sentence or a logical form) is defined as: with Z(x) as (Ω(x) are all derivations for input x): the constraint put on feature values during training only refers to derivations with the same input.


In contrast to directional models, we impose the two constraints per feature given in figure 1: one on the feature value with respect to the sentences S in the parse disambiguation treebank and the other on the feature value with respect to logical forms L in the fluency ranking treebank.

of the constraints on training defined in figure 1, the feature weights in the reversible model distinguish, at the same time, good parses from bad parses as well as good realizations from bad realizations.


By minimizing an arc-level hinge loss with latent events (which in turn correspond to token-roles), we assign costs to tokenroles based on arc accuracy.

1) A token-level decision that affects multiple arcs impacts multiple instances of hinge loss, and 2) No extra credit (penalty) is given for multiple decisions that (in)correctly filter the same arc.


12 Graphbased and transition-based systems tend to make different types of errors (McDonald and Nivre, 2007).

having fast, accurate parsers for both approaches presents an opportunity for large-scale, robust parser combination.


However, they are easy to train, are long-studied and well understood, and can be efficiently incorporated into search procedures, such as for machine translation.

the output of such text generation systems is often very poor grammatically, even if it is understandable.


If we have a grammar comprising small and large fragments, we might consider that larger fragments should be less likely to fit into ungrammatical situations, whereas small fragments could be employed almost anywhere as a sort of ungrammatical glue.

on average, grammatical sentences will license deriva-tions with larger fragments, whereas ungrammatical sentences will be forced to resort to small fragments.


The imaginary root node at the center represents the information need, and we assume that the need is propagated through this graph, where edges represent within-sentence co-occurrences.

to compute sentence scores, we use not only the query terms but also the words in R1 and R2.


Our own implementation of an MMR-based baseline uses an existing algorithm to maximize the following summary set score function (Lin and Bilmes, 2010): where v D is the vector representing the source documents, v Q is the vector representing the query terms, Sim is the cosine similarity, and γ is a parameter.

the first term of this function reflects how the sentences reflect the entire documents; the second term reflects the relevance of the sentences to the query; and finally the function penalizes redundant sentences.


From Table 2, it can be observed that both QSBP and QSBP(idf) significantly outperforms QSBP(nodist), QSB, WP and the baseline in terms of all evaluation metrics.

the minimum dependency distance, Query Snowball and the use of word pairs all contribute significantly to the performance of QSBP.


IV Combination Putting the verbalised dialogues acts together to create a complete dialogue, and V Presentation: Rendering of the dialogue (this can range for simple textual dialogue scripts to computer-animated spoken dialogue).

1 computing.open.ac.uk/coda/data.html 2 the corpus was not constructed entirely of pre-existing text; some of the text was authored as part of the corpus construction.


Moreover, for each event type, the event participants and attributes may also appear in multiple forms and exemplars of the different forms may be required.

event extraction is a difficult task and requires substantial training data.


Annotators need to read the whole sentence, possibly several sentences, to decide whether there is a specific event or not, and then need to identify the event participants (like Agent and Patient), and attributes (like place and time) to complete an event annotation.

for event extraction tasks like MUC4, MUC6 (MUC 1995) and ACE2005, from one to several hundred annotated documents were needed.


The classifier is re-trained and the procedure repeated.

the criterion for selecting the most confident examples is critical to the effectiveness of self-training.


(Ji and Grishman 2008) have observed that the events in a cluster of documents on the same topics as documents in the training corpus can be tagged more confidently.

we believe that bootstrapping on a corpus of topic-related documents should perform better than a regular newswire corpus.


However these patterns are often too broad, resulting in the extraction of tuples that do not represent relations at all.

heavy (supervised) postprocessing or use of supervised information is necessary.


As we can see from the criteria for extracting SSRs, all the entries in the SSR rule set satisfies the commonly used phrase extraction heuristics.

the set of SSRs is a subset of the phrase table.


The language model score of the whole sentence is useless because it is dominated by words not affected by the substitution.

we only consider n-grams that are affected by the substitution.


As we have abundant sentence pairs generated, we prefer to apply a more harsh filtering, keeping only the best candidates.

when training the SVM model, we intentionally increase the cost of false positive errors, so as to maximize the precision rate of positive decisions and reduce possible contamination.


That is because longer sentences have more slots for substitution.

they have more occurrences in the generated corpus.


#499: The supports relation is transitive.

because Putin supports the United Russia party, and the United Russia party supports Medvedev, we can infer that Putin supports Medvedev.


Fortunately, however, it appears that at least a very large fraction of commonsense knowledge can be captured by the sets of categories we describe here.

these categories serve to point out promising areas for further research in collecting common-sense knowledge.


It is known that culture, age and gender affect people's nonverbal behaviors (Linda L. Carli and Loeber, 1995;Matsumoto, 2006).

there might be variations among people's reactions even when experiencing the same situation.


Although listener responses vary among individuals, we expect some patterns in these responses.

we first analyze the most predictive features for each listener and search for prototypical patterns (in Section 3.1).


The speaker videos were transcribed and annotated to extract the following features: Lexical: Some studies have suggested an association between lexical features and listener feedback (Cathcart et al., 2003).

we use all the words (i.e., unigrams) spoken by the speaker.


Before reviewing the prediction results, is it important to remember that backchannel feedback is an optional phenomena, where the actual listener may or may not decide on giving feedback (Ward and Tsukahara, 2000).

results from prediction tasks are expected to have lower accuracies as opposed to recognition tasks where labels are directly observed (e.g., part-of-speech tagging).


That means that despite the fact that the training data contained some incorrectly annotated tokens, the parsers were able to annotate them differently.

we suggest that the recall of our method is close to the value of 0.459.


The interesting question which naturally arises at this point is whether the errors we find are the same as those found by the method of variation detection.

we have performed the following experiment: We have counted the numbers of occurrences for the dependencies B  A (the word B is the head of the word A) and C  A (the word C is the head of the word A), where B  A is the dependency proposed by the parsers and C  A is the dependency proposed by the gold standard.


We will provide some of the example errors, which we are able to find with our approach.

we will provide the sentence strings and briefly compare the gold standard dependency annotation of a certain dependency within these sentences.


For example, in TempEval-2 there were 6 subtasks (event extraction, temporal expression extraction and 4 subtasks on identifying temporal relations).

different systems perform best is different subtasks, but we can't compare overall performance of systems.


Each definition contains the Word, POS, and Synset list: (funny (adjective (root funny) (orth "funny") (synset ("funny%4:02:01::" "funny%4:02:00::"))))) WordNet only contains open-class words: Nouns, Adjectives, Adverbs, and Verbs (Miller et al, 1993).

determiners, subordinating conjunctions, coordinating conjunctions, and pronouns all had to be hand created.


With proper noun and number coverage, total failures would have been reduced by 48%.

simple automated additions in the future can greatly enhance performance.


If we assume independence, then the chance that none of the 5 annotators agrees with each other (majority class size of 1) is 1 × 10/11 × 9/11 × 8/11 × 7/11 = 0.344.

if there was no correlation among any of the terms and colours, then 34.4% of the time none of the annotators would have agreed with each other.


Observe that the neg-ative terms are strongly associated with black and red, whereas the positive terms are strongly associated with white and green.

colour can add to the potency of emotional concepts, yielding even more effective visualizations.


A similar contradiction occurs if s is an inverted t-split.


Thus s should be the unique t-split for A.

by I.H., S 1 and S 2 have a unique derivation, because their lengths are less than n. the derivation for A will be unique.


The results are very similar to the those CKK reported (baseline 25.2, reordered 26.8).

the HD reimplementation is indeed close to the original CKK system.


1 can be rewritten aŝ As t is a non-deterministic input (contrarily to κ), t needs to be decoded in a word d of the vocabulary.

we must marginalise for every possible decoding: Furthermore, by applying simple Bayes transformations and making reasonable assumptions, The first term in Eq.


The HTR subsystem mistakenly recognises in.

the user falls back to the keyboard and types is.


First, the publicly available IMT decoder Thot (Ortiz-Martínez et al., 2005) 1 was used to run an off-line simulation for keyboardbased IMT.

a list of words the system 1 http://sourceforge.net/projects/thot/ Spanish English dev test dev test independent HTR ( †) 9.6 10.9 7.7 9.6 decoupled ( ) 9.5 10.8 7.2 9.6 best coupled 6.7 8.9 5.5 7.2 Table 1: Comparison of the CER with previous systems.


Second, with the presence of pruning, dynamic programming states that are generated by rules with many target-side terminals are disadvantaged when competing with others in the same bin because of the language model score.

these would be discarded earlier, even if the original unbinarized rule has a high probability.


As a result, these would be discarded earlier, even if the original unbinarized rule has a high probability.

we lose the benefit of using larger rules, which have more contextual information.


Because SCFGs neither model adjunction, nor are they markovized, with a small amount of data, such problems can occur.

we may opt to add glue rules as used in Hiero (Chiang, 2005): where S is the goal state and C is the glue nonterminal that can produce any nonterminals.


However, it is difficult to predict what should be translated as a single block.

how to specify ranges for reordering constraints is a very important problem.


We then sketched an implementation that improves the time and space efficiency of our method by pre-computing and "sparsifying" n-gram projections off-line during the training phase.

our approach can be integrated within on-line, low-latency SMT systems.


In the regular MSD model, the previous orientation for a phrase pair is monotonous if the previous word in the source phrase is aligned with the previous word in the target phrase and not aligned with the next word.

the probability of a phrase pair to have a monotonous orientation P c (M ) is given by the probability of the previous word in the source phrase being aligned with the previous word in the target phrase W n−1 i−1 , and the probability of the previous word in the source to not be aligned with the next word in the target (1 − W n−1 j+1 ).


1 The reason for this is that the Chinese words do not have any marking for number.

the information needed to mark friend and partner for number must come from the context.


(Table 2, columns 3-4), which ignore context, have a strong preference for plural translations, except in the case of t(朋友 | friend).

we hypothesize that, for Chinese-English translation, we should weaken the lexical weighting features' morphological preferences so that more contextual features can do their work.


Take Strict convexity requires that the former expression be strictly smaller than the latter, but we have equality.


Because the objective is convex, the inequality constraints are convex, and the equality constraints are affine, the IBM Model 1 optimization problem is a convex optimization problem.

every local optimum is a global optimum.


Concerning the interpretation of these findings, we note that the two taggers were both native speakers of languages which make an overt T/V distinction.

our present findings cannot be construed as firm evidence that English speakers make a distinction, even if implicitly.


Threshold is set to 2 in this paper, as Damerau (1964) shows that about 80% of typos are caused by a single edit operation.

using a threshold of 2, we should be able to find most of the typos.


When as-  sessing coverage, it is only meaningful to compare reports on the same scenario.

we regard our dataset as 8 collections (Scenario A to Scenario H), each containing 7 reports.


While existing web-based peer-review systems largely save instructors effort in setting up peer-review assignments and managing document assignment, there still remains the problem that the quality of peer reviews is often poor (Nelson and Schunn, 2009).

to enhance the effectiveness of existing peer-review systems, we propose to automatically predict the helpfulness of peer reviews.


Note, however, that peer-review helpfulness is rated for the whole review, which can include multiple idea units.

2 in our study, we calculate the distribution of feedbackType values (praise, problem, and summary) (kappa = .92), the percentage of problems that have problem localization -the presence of information indicating where the problem is localized in the related paper-(kappa = .69), and the percentage of problems that have a solutionthe presence of a solution addressing the problem mentioned in the review-(kappa = .79) to model peer-review helpfulness.


(2009) focused on the review comments, we hypothesize that there is also a difference in perceived peer-review helpfulness.

we are planning to investigate the impact of these different helpfulness ratings on the utilities of features used in modeling peer-review helpfulness.


When it must decide between what it views as two equally likely possibilities, it defaults to OK.

even though LM has higher unweighted precision (0.957) than PERC (0.813), it is only really better on the most clear-cut cases (the 90-100% bin).


On the other hand, a system using "textual information" may be effective in the first case, but is probably weak in finding friends in the second case.

in this paper, we propose to use multiple information sources for group member suggestions, and use a cross-validation approach to find the best-fit sub-system for the final suggestion.


However, since the space of potential segmentations grows exponentially with the length of the corpus, no tractable algorithm follows directly from the Minimum Description Length (MDL) principle.

it is necessary to generate a set of candidate segmentations and select between them according to the MDL principle.


Unfortunately, evaluating all possible segmentations is intractable, since a corpus of length n has 2 n−1 possible segmentations.

mDL methods have to rely on an efficient algorithm to generate a relatively small number of candidate segmentations to choose between.


MDL is analogous to Bayesian inference, where the information cost of the model CODE(M ) acts as the prior distribution over models P (M ), and CODE(D|M ), the information cost of the data given the model, acts as the likelihood function P (D|M ).

bayesian word segmentation methods may be considered related as well.


The percentage of results marked as redundant for the BCB-S, BR, and PD approaches were 22.6%, 52.5%, and 22.9%, respectively.

the BR system, which appeared to have excellent (lenient) precision in our initial evaluation, returns a very large number of redundant paraphrases.


The usual drawback in the first sense heuristic applied to the WordNet is the small size of the SemCor corpus.

senses that do not occur in SemCor are often ordered arbitrarily.


Our approach is automated, and requires only documents assigned to domains/categories, such as Reuters corpus, and a dictionary with gloss text, such as WordNet.

it can be applied easily to a new domain, sense inventory or different languages, given sufficient documents.


We note that the matrix M is a high-dimensional space.

we used a ScaLAPACK, a library of high-performance linear algebra routines for distributed memory MIMD parallel computing (Netlib, 2007) 2 .


There are no existing sense-tagged data for these 20 categories that could be used for evaluation.

we selected a limited number of words and evaluated these words qualitatively.


We note that the corpus used in our approach is different from SFC.

recall denotes a ratio of the number of senses matched in our approach and SFC divided by the total number of senses appearing in both SFC and Reuters.


For each word, we    Table 3: The results against SFC resource selected 10 sentences from the documents belonging to each corresponding category.

we tested 100 sentences for each category.


However, when the number of covered hypothesis terms increases the probability that the remaining terms are actually entailed by T increases too (even though we do not have supporting knowledge for their entailment).

an alternative model is to group all uncovered terms together and estimate the overall probability of their joint entailment as a function of the lexical coverage of the hypothesis.


The latter, however, has its shortcomings in that the coarse-grained sentiment signal is less informative compared to a fine-grained signal.

in order to get the best of both worlds, we would like to combine the merits of both of these models.


It is unlikely that a positive opinion word will modify "valley", e.g., "good valley" in this context.

if a product feature is modified by both positive and negative opinion adjectives, it is unlikely to be an opinionated product feature.


As shown in Table 2, one can see that 38 DA types achieve the best performance for the proposed detection model.

we use 38 DA types (q = 38) in our system.


Furthermore, while the rate of verbal feedback differs across language and speaker, it is, overall, a relatively infrequent phenomenon, occurring in as little as zero percent of pausal intervals for some dyads and only at an average of 13-30% of pausal intervals across the three languages.

the substantial class imbalance and relative sparsity of listener verbal feedback present challenges for data-driven machine learning methods.


Following on, the word may is likely to be a month rather than a modal verb, although the latter is more frequent and will dominate in BO .

we have much less faith inp(w i |BO ) than iñ p(w i |BO ) and would like a much smaller weight γ assigned to BO , but it is not possible in the backoff scheme in Eq.


φ m , we have the same distribution: Note that the last transformation is because φ m ⊂ φ m−1 ; had it not been the case,p m would depend on the combination of φ m and φ m−1 and require multiple parameters to be represented on its entire domain w n 1 ∈ φ m .

after n iterations, we have: we have constructedp n (w i |φ n ) using the same recursive representation as in Eq.


It scales almost as well to large datasets as standard N-Gram models: training requires only two passes over the data as opposed to a single pass required by N-Gram models.

the experiments provide empirical evidence that the VMM is based on a reasonable set of modeling assumptions, which translate into an accurate and scalable model.


One noteworthy feature of our method is that there is no need to handle sparse and high-dimensional feature spaces often used in many supervised NLP systems, which is one of the main causes of the data sparseness problem often encountered when we learn the model with a supervised leaning algorithm.

nLP systems that are both compact and highperformance can be built by retraining the model with the obtained condensed feature set H. Let us first define the condensed feature set H. In this paper, we call the feature set generally used in supervised learning, F, the original feature set.


We can now be quite confident that search engines like Google will return several pages relevant to our queries, but rarely does one have time to go through the enormous amount of data that is supplied.

automatic text summarization, which aims at providing a shorter representation of the salient parts of a large amount of information, has been steadily growing in both importance and popularity over the last several years.


This results in a smoothed language model for each document set's content distribution where the counts from content words (as determined through inference) are used to determine their probability, and the syntax words are essentially discarded.

our model describes the process of generating a document as traversing an HMM and  emitting either a content word from a single topic's (document set's) content word distribution, or a syntax word from one of C corpus-wide syntax classes where C is a parameter input to the algorithm.


Nevertheless, adjectives and verbs also often carry important semantic information.

we ran a POS tagger over the input sentences and tried selecting sentences based on word distributions that included only nouns; nouns and verbs; nouns and adjectives; and nouns, verbs, and adjectives.


With the cross-product approach, fewer active states in either child cell leads to fewer grammar access operations.

pruning constituents in lower cells directly affects the overall efficiency of parsing.


This is the covariate shift assumption (Shimodaira, 2000).

each sentence should be weighted by Pt(x) Ps(x) where P t is the target distribution, and P s the source distribution.


In addition to the regular beam pruning used in traditional ASR (Ortmanns et al., 1997), search paths which are inconsistent with the haptic events are also pruned away to achieve further reduction in the recognition search space.

the runtime of HVR is generally more efficient than ASR.


Similar to having an acoustic model as a statistical representation of the phoneme sequence generating the observed acoustic features, a haptic model is used to model the PLI sequence generating the observed haptic inputs, H. The haptic likelihood can be factorised as where  However, it is also possible to have a non-diagonal matrix for p(h i |l i ) in order to accommodate typing errors, so that non-zero probabilities are assigned to cases where For handwriting input, h i denote a sequence of 2dimensional feature vectors, which can be modelled using Hidden Markov Models (HMMs) (Rabiner, 1989).

(h i |l i ) is simply given by the HMM likelihood.


board and keystroke inputs respectively.

there are altogether four input configurations.


However, since the speech and haptic inputs are provided concurrently, the resulting total input speed for asynchronous HVR is much slower compared to synchronous HVR.

synchronous HVR is potentially more efficient than asynchronous HVR.


For keystroke input, top five letter candidates returned by the handwriting recognizer were used.

in clean condition, the acoustic models are able to recover some of the errors introduced by the handwriting recognizer, bringing the LER down to as low as 0.3%.


The average number of active tokens also reduces to the range of 3490 -4442.

the synchronous HVR using keyboard input is robust to noisy environment, both in terms of WER and runtime performance.


The runtime performance using keystroke input is also comparable to that using keyboard input (only slightly worse).

both keyboard and keystroke inputs are effective ways for entering the initial letters for HVR.


Partial Lexical Information (PLI), such as the initial letter of the words, are used as cues to guide the recognition search process.

apart from the acoustic and language models used in conventional ASR, HVR also combines the haptic model as well as the PLI model to yield an integrated probabilistic model.


Unfortunately, these necessary data are only available for a very small number of languages in the world.

a procedure for training acoustic models without annotated data would not only be a breakthrough from the traditional approach, but would also allow us to build speech recognizers for any language efficiently.


Dirichlet processes are particulary suitable for our goal.

we construct our model as a Dirichlet Process (DP) mixture model, of which the components are HMMs that are used prior distribution for cluster labels the index of the boundary variable with value 1 € q th Figure 2: The graphical model for our approach.


However, numerous related studies have reported performance on the TIMIT corpus (Dusan and Rabiner, 2006;Estevan et al., 2007;Qiao et al., 2008;Zhang and Glass, 2009;Zhang et al., 2012), which creates a set of strong baselines for us to compare against.

the TIMIT corpus is chosen as the evaluation set for our model.


This examination form combines both of the two forms described above.


These words will usually be the key words in the BOW methods, which will lead to a great disturbrance for the methods.

the conventional BOW methods are no longer appropriate because of the characteristic of the ASR result.


It is very useful in expressing the sequences and the hierarchy in picture composition.

we build a FST-based model to extract features related to the transcription assessment in this paper.


The FST model is very powerful in delivering the sequence information that a meaningless sequence of words related to the topic content will get low score under the model.

it works well concerning the second problem.


The LSA (Deerwester et al., 1990) considers the relations between the dimensions in conventional vector space model (VSM) (Salton et al., 1975), and it can order the importance of each dimension in the Latent Semantic Space (LSS).

it is useful in reducing the dimensions of the vector by truncate the high dimensions.


The acoustic level only works well in excellent speeches (Huang et al., 2010).

this paper mainly focuses on the integrity   Each speech in our experiments was scored by three raters.


The first given sentence in the examination is labeled with 0m and the other describing sentences for the 1 to 4 pictures are labeled with 1m to 4m, while the elaboration ones for the 4 pictures are labeled with 1s to 4s.

each sentence in the composition is labeled as a sense-group.


However, the performance's reduction of the AES system indicates that the similarity is not good enough to extend the FST model.

we seek for human help to accurate the similarity calculation.


When scoring the speech, human raters concern more about how much valid information it contains and irrelevant contents are not taken for penalty.

the match features are more reasonable than the edit distance features.


In the extension, the similarities of the synonyms  Table 7: Performance of the FST Method and the Improved FST Method describe the invisible (extended) part of the FST, so it should be very accurate for the substitutions cost.

we added manual intervention to the similarity result calculated by the wordnet::similarity software packet.


Since EDU boundaries are highly correlated with the syntactic structures embedded in the sentences, EDU segmentation is a relatively trivial step -using machine-generated syntactic parse trees, HILDA achieves an F-score of 93.8% for EDU segmentation.

our work is focused on the treebuilding step, i.e., the Structure and the Relation classifiers.


Although perfect contextual features are available only in idealized situations, when they are correct, together with other features, they can almost correctly predict the tree structure and better predict the relation labels.

an iterative updating approach, which progressively updates the tree structure and the labeling based on the current estimation, may push the final results toward this idealized end.


Macro-averaged F-score is not influenced by the number of instances that exist in each relation class, by equally weighting the performance of each relation class 3 .

the evaluation is not biased by the performance on those prevalent classes such as ATTRIBUTION and ELABORATION.


multinomial distributions over words) {ψ c,t,n } that generate words in turn t and the topics {ψ c,t−1,n } that generate words in turn t − 1 come from the same distribution.

all topics used in a segment s are drawn from a single distribution, For notational convenience, S c denotes the number of segments in conversation c, and s t denotes the segment index of turn t. We emphasize that all segment-related notations are derived from the posterior over the topic shifts l and not part of the model itself.


Each clause in a turn is coded with a Question Topic (T Q ) and a Response Topic (T R ).

a turn has a list of T Q 's and T R 's both of length equal to the number of clauses in the turn.


Many algorithms (e.g., probabilistic approaches) give non-binary segmentations where candidate boundaries have real-valued scores (e.g., probability or confidence).

evaluation requires arbitrary thresholding to binarize soft scores.


As  only 40 instances of OVERLAP relations were annotated when neither INCLUDES nor IS INCLUDED label matched, for evaluation purposes all instances of these relations were merged into the temporally coarse OVERLAP relation.

the total number of OVERLAP relations in the corpus grew from 40 to 258 annotations in total.


Each node of G C clusters together coreferent nodes, representing a discourse referent.

a node u in G C is a cluster of nodes u 1 , .


An important type of entailment rule specifies the entailment relation between natural language predicates, e.g., the entailment rule 'X invade Y → X attack Y' can be helpful in inferring the aforementioned hypothesis.

substantial effort has been made to learn such rules (Lin and Pantel, 2001;Sekine, 2005;Szpektor and Dagan, 2008;Schoenmackers et al., 2010).


Despite this progress, finding the exact solution remains NP-hard -the authors themselves report they were unable to solve some graphs of rather moderate size and that the coverage of their method is limited.

scaling their algorithm to data sets with tens of thousands of predicates (e.g., the extractions of Fader et al.


The entailment relation, described by entailment graphs, is typically from a "semantically-specific" predicate to a more "general" one.

intuitively, the topology of an entailment graph is expected to be "tree-like".


However, we hypothesize that this scenario is rather uncommon.

a natural variant of the Max-Trans-Graph problem is to restrict the required output graph of the optimization problem (1) to an FRG.


Moreover, only a direct child of c can choose v as a parent instead of c (Figure 3b), since for any other descendant of c, v would become a second parent, and G red will no longer be a directed forest (Figure 3b').

this case requires adding in G edges from v to all nodes in c and their ancestors, and also for each new child of v, denoted by d ∈ V red , we add edges from all nodes in d and their descendants to v. Crucially, although the number of possible subsets of c's children in G red is exponential, the fact that they are independent trees in G red allows us to go over them one by one, and decide for each one whether it will be a child of v or not, depending on whether S v-in (d) is positive.


Thus, this case requires adding in G edges from v to all nodes in c and their ancestors, and also for each new child of v, denoted by d ∈ V red , we add edges from all nodes in d and their descendants to v. Crucially, although the number of possible subsets of c's children in G red is exponential, the fact that they are independent trees in G red allows us to go over them one by one, and decide for each one whether it will be a child of v or not, depending on whether S v-in (d) is positive.

the score (3) in this case is: where child(c) are the children of c. Case 3: Inserting v as a new root in G red .


Similar to case 2, only roots of G red can become children of v. In this case for each chosen root r we add in G edges from the nodes in r and their descendants to v. Again, each root can be examined independently.

the score (3) of re-attaching v is: where the summation is over the roots of G red .


Since re-attachment is linear, re-attaching all nodes is quadratic.

if we bound the number of iterations over all nodes, the overall complexity is quadratic.


Nevertheless, the ILP in GNF is simpler than (1), since we consider only candidate edges involving v. Figure 4 illustrates the three types of possible transitivity constraint violations when reattaching v. The left side depicts a violation when (i, k) / ∈ E, expressed by the constraint in (9) below, and the middle and right depict two violations when the edge (i, k) ∈ E, expressed by the constraints in (10).

the ILP is formulated by adding the following constraints to the objective function (3): Complexity is exponential due to the ILP solver; however, the ILP size is reduced by an order of magnitude to O(|V |) variables and O(|V | 2 ) constraints.


On the other hand, planning with low-level actions does not suffer from this limitation, but is computationally intractable for even moderately complex tasks.

in many practical domains, planning algorithms rely on manually-crafted high-level abstractions to make search tractable (Ghallab et al., 2004;Lekavý and Návrat, 2007).


In contrast, our focus is on learning the abstraction from natural language.

our technique is complementary to past work, and can benefit from human knowledge about the domain structure.


We assume that we are given a seed mapping between a predicate x i , and the word types in the document that reference it (see Table 3 for examples).

for each predicate pair x k , x l , we want to utilize the text to predict whether x k is a precondition for x l ; i.e., x k → x l .


We assume that our reward signal r strongly correlates with the correctness of model predictions.

during learning, we need to find the model parameters that maximize expected future reward (Sutton and Barto, 1998).


Note that, following standard practice, descriptor clustering was performed ignoring the region partition, but the resulting visual words correspond to different dimensions in the concatenated BoVW vectors, depending on the region in which they occur.

a vocabulary of k visual words results in BoVW vectors with k × 16 dimensions.


However, we conduct CRF inference in tandem with the translation decoding procedure ( §3), creating an environment in which subsequent words of the observation are not available; the MT system has yet to generate the rest of the translation when the tagging features for a position are scored.

we only define emission features on the observed words at the current and previous positions of a class: φ o (τ i , e i , e i−1 ).


The dependency parser and POS tagger are trained on supervised data and up-trained on data labeled by the CKY-style bottom-up constituent parser of , a state of the art broadcast news (BN) parser, with phrase structures converted to labeled dependencies by the Stanford converter.

while accurate, the parser has a huge grammar (32GB) from using products of latent variable grammars and requires O(l 3 ) time to parse a sentence of length l. we could not use the constituent parser for ASR rescoring since utterances can be very long, although the shorter up-training text data was not a problem.


We divided these candidates into two classes: those whose score is greater than a threshold and the other ones.

a given word in the corpus can be associated with different properties whether it belongs to a potential collocation: the class c and the internal structure cs of the collocation it belongs to, its position cpos in the collocation (B: beginning; I: remaining positions; O: outside).


Despite the existence of multiple resources, such data cannot be simply put together for training systems, because almost all of statistical NLP systems assume homogeneous annotation.

it is not only interesting but also important to study how to fully utilize heterogeneous resources to improve Chinese lexical processing.


Word clustering derives paradigmatic relational information from unlabeled data by grouping words into different sets.

the contribution of word clustering to POS tagging is two-fold.


They signal the structural relationships that words have to one another and are the glue that holds sentences together.

they serve as important elements to the structures of sentences.


For example, in part-of-speech tagging tasks, there can be more than 40 labels and more than 1,600 types of label transitions.

incorporating local observation information into the edge feature will result in an explosion of edge features, which is 1,600 times larger than the number of feature templates.


There are only nine possible label transitions: T = Y × Y and |T| = 9.

2 the feature dimension will have nine times increase over the feature templates, if we incorporate local observation information of x x x into the edge features.


In most cases, documents in a text collection are full articles.

a WSD system has sufficient context to disambiguate the words in the document.


With sense incorporated, the term frequency is influenced by the sense information.

the estimation of probability of t in d becomes query specific: where the probability of t in C is re-calculated as: Words usually have some semantic relations with others.


With this language model, the probability of a query term in a document is enlarged by the synonyms of its senses; The more its synonym senses in a document, the higher the probability.

documents with more synonym senses of the query terms will get higher retrieval rankings.


The Chinese translation in the latter sense represents the meaning of the phrase in original query.

stem prf +WsD outperforms the other two methods on this query by assigning the highest probability for this sense.


Moreover, even if two transformations change the same number of nodes and edges, their costs might be significantly different.

the measurement of the cost accumulated so far (g(s)) and the remaining gap to t H (h(s)) are unrelated.


The comparison of costs requires that all experiments are performed on the same model which was learned during training.

in the training phase we used the original search of BIUTEE, and then ran the test phase with each algorithm separately.


This is because the hidden structure such as phrase segmentation and alignment could be abused if the system is forced to produce a reference translation.

instead of pushing the parameter update towards the reference translation (a.k.a.


is proportional (with a factor of N) to the expected sentence BLEU score over the entire training set, i.e., after some algebra, In a phrase-based SMT system, the total number of parameters of phrase and lexicon translation models, which we aim to learn discriminatively, is very large (see Table 1).

regularization is critical to prevent over-fitting.


, we have the following auxiliary function: After substituting (2) and (7) into (6), and drop optimization irrelevant terms in KL regularization, we have in a rational function form: are all positive polynomials of .

we can follow the two steps of EBW to derive the GT formulas for .


Due to mismatch between training and tuning data, the training process might not always converge.

we need a validation set to determine the stop point of training.


In their graph, a node does not represent only a source sentence but a pair of source sentence and its candidate translation, and there are only two possible labels for each node, namely, 1 (this is a good translation pair) and 0 (this is not a good translation pair).

their graph-based model is a normal example of the general graph-based model.


It is not difficult to handle test nodes, since the purpose of MT decoder is to get all possible segmentations of a source sentence in dev/test data, search for the translation candidates of each source span, and calculate the probabilities of the candidates.

the cells in the search space of a decoder can be directly mapped as test nodes in the graph.


In order to evaluate all the expression types, we follow the binary approach which is an extension of one-against-all method for multi-label classification.

for each label, we build a binary classification problem.


However data sources in VPA systems pose new challenges, such as variability and ambiguities in natural language, or short utterances that rarely contain contextual information, etc.

sLU plays an important role in allowing any sophisticated spoken dialog system (e.g., DARPA Calo (Berry et al., 2011), siri, etc.)


Previous work (Asuncion et al., 2009;Wallach et al., 2009) shows that the choice of inference method has negligible effect on the probability of testing documents or inferred topics.

we use Markov Chain Monte Carlo (MCMC) method,specifically Gibbs sampling, to model the posterior distribution drawn from this distribution.


However, aspects are often more probable than sentiments in a sentence (e.g., "The beds, sheets, and bedding were dirty.").

it is more principled to employ asymmetric priors.


However, we usually do not have evidence for inferred facts as well as noisy-or nodes.

there are a number of variables in the ground networks which are always hidden, and hence EM is appropriate for learning the requisite parameters from the partially observed training data.


9 Second, unlike summarization, we are not given with a set of coherent text snippet to begin with, and the level of noise coming from the visual recognition errors is much higher than that of starting with clean text.

choosing an additional phrase in the image description is much riskier than it is in summarization.


As shown in Table 1, the mapping from d to w is unknown.

all the intermediate multinomial distributions, described in the previous section, define a hidden correspondence structure h, between records, fields, and their values.


The key insight is that each node in the tree functions as a highly compact information-rich summary of its children.

a small handful of upper-level nodes may summarize millions of mentions (for example, a single node may summarize all contextually similar "R. Hamming" mentions).


Since inference must determine the structure of the entity trees in addition to coreference, it is advantageous to consider multiple MH proposals per sample.

we employ a modified variant of MH that is similar to multi-try Metropolis (Liu et al., 2000).


Furthermore, intuitively, there are some general syntactic relationships or patterns between topic and sentiment words across different domains.

if we can mine the patterns from the source and target domain data, then we are able to construct an indirect connection between topic words across domains by using the common sentiment words as a bridge, which makes knowledge transfer across domains possible.


KRISP learns string-kernel classifiers that maps natural language substrings to MRG production rules.

it is important that the production rules in the MRG mirror the structure of natural language (Kate, 2008).


There are two types of data we are interested in collecting: natural language navigation instructions and follower data.

we created two tasks on Mechanical Turk.


The posterior distribution over elementary trees given a parse tree t can be computed by using the Bayes' rule: where p (t |e ) is either equal to 1 (when t and e are consistent) or 0 (otherwise).

the task of TSG induction from parse trees turns out to consist of modeling the prior distribution p (e).


This result suggests that the SR-TSG rules extracted from the training set are very sparse and cannot cover the space of unknown syntax patterns in the testing set.

sophisticated backoff modeling is essential for the SR-TSG parser.


Our model can be viewed as an extension of Cohn's work by the incorporation of symbol refinement.

this result confirms that a TSG and symbol refinement work complementarily in improving parsing accuracy.


5, we haveK whereφ To getK k , we just need to compute the weighted sum of re-writing rules for double lists matching (α s 1 ⊗ α s 2 , α t 1 ⊗ α t 2 ).

we can work on the "combined" pair of double lists instead of two pairs of k-grams.


(2007) 65.75 Heilman and Smith (2010) 62.8 Our baseline (PR) 62.0 Our method (ps-SRK) 64.6 Our method (pw-SRK) 63.8 Our method (kb-SRK) 65.1  neering efforts.

we only compare with the six systems which involves less engineering.